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Security Audit

ralph-loop

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
50
CAUTION
Scanned about 2 months ago
2
Critical
Immediate action required
1
High
Priority fixes suggested
1
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

ralph-loop received a trust score of 50/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 4 findings: 2 critical, 1 high, 1 medium, and 0 low severity. Key findings include Hidden network beacons / undisclosed telemetry, Command Injection via RALPH_FLAGS environment variable, Command Injection via RALPH_TEST environment variable.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 33/100, indicating areas for improvement.

Last analyzed on February 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
85%
Static Code Analysis
100%
Dependency Graph
100%
LLM Behavioral Safety
33%

Behavioral Risk Signals

Network Access
1 finding
Filesystem Write
1 finding
Shell Execution
4 findings
Dynamic Code
2 findings

Security Findings4

SeverityFindingLayerLocation

Scan History

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